Businesses constantly strive to gain information about consumers in order to customize the manner in which they engage with those consumers. For example, businesses regularly develop customized advertisements (i.e., “targeted ads”) seeking to appeal to a specific class of consumers based upon analyzed consumer data. Historically, businesses have relied upon information such as demographic data, geographic data, behavioral data, contextual data, and timing data in order to customize engagement with consumers. However, over-reliance on these conventional types of consumer data may limit the effectiveness of the engagement.
For example, according to a recent survey conducted by Econsultancy, more than two-thirds (68%) of companies are targeting customers based on demographic data, while sixty-four percent (64%) of companies are utilizing geographic data about consumers to generate targeted advertising. Thus, demographic data and geographic data continue to be the preferred types of data for businesses to use in developing targeted advertisements and the like. This is largely due to the fact that demographic data and geographic data have conventionally been the easiest types of consumer data to obtain. For example, businesses routinely gather these types of information through customer loyalty programs and customer satisfaction surveys. However, there are inherent drawbacks associated with over-reliance on these types of consumer information for purposes of targeting consumers with customized content.
For example, consumers are becoming increasingly protective of demographic and geographic data due to privacy concerns. Indeed, many governments have taken legislative action to address these privacy concerns. As such, demographic and geographic data are not as easily obtained in the present day as they once were. Furthermore, these privacy concerns may be well-founded. For example, studies indicate that analysis of basic demographic information (e.g., gender, ZIP code, and date of birth) allows for unique identification of sixty-three percent (63%) of the U.S. population. Moreover, research indicates that demographic and geographic data do not directly determine the consumption choices of consumers. For example, being in a certain age group or residing in a certain ZIP code are not necessarily the reasons why a consumer purchases a given product or service.
In recent years, increased attention has been paid to the value of consumer interest information. As used herein, an “interest” may include any topic that a given person/entity feels favorably or unfavorably towards. For example, an interest could include products (e.g., books), types of products (e.g., non-fiction books), people (e.g., political figures), activities (e.g., skiing), businesses/brands (e.g., Apple®), etc. Consumer interest information has received increased attention due, in part, to the fact there is a direct and causal relationship between consumers' interests and consumers' consumption behavior. That is to say, unlike demographic and geographic data about consumers, interest information about consumers can actually be used to predict consumption behavior (e.g., what products/services a consumer is likely to buy).
However, conventional methods and systems are unable to harness the utility of consumer interest information. Accordingly, it is desirable to provide techniques for generating and using an interest graph that overcome many of the drawbacks associated with conventional techniques for targeted engagement with consumers.